A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system

Neuromorphic perception systems inspired by biology have tremendous potential in efficiently processing multi-sensory signals from the physical world, but a highly efficient hardware element capable of sensing and encoding multiple physical signals is still lacking. Here, we report a spike-based neuromorphic perception system consisting of calibratable artificial sensory neurons based on epitaxial VO2, where the high crystalline quality of VO2 leads to significantly improved cycle-to-cycle uniformity. A calibration resistor is introduced to optimize device-to-device consistency, and to adapt the VO2 neuron to different sensors with varied resistance level, a scaling resistor is further incorporated, demonstrating cross-sensory neuromorphic perception component that can encode illuminance, temperature, pressure and curvature signals into spikes. These components are utilized to monitor the curvatures of fingers, thereby achieving hand gesture classification. This study addresses the fundamental cycle-to-cycle and device-to-device variation issues of sensory neurons, therefore promoting the construction of neuromorphic perception systems for e-skin and neurorobotics.

We would like to sincerely thank the editor for the kind consideration of our manuscript and the reviewers for their constructive suggestions which are very valuable in improving our manuscript. We have carefully considered all the reviewers' questions and made corresponding revisions. Additional experiments, modeling, and simulations (Supplementary Fig. 2,4,5,14,15,(21)(22)(23)(24), Equation 1-10) are performed. We have also improved the writing and presentation of the manuscript. We hope the editor and reviewers will find the revised manuscript suitable for publication in Nature communications. The point-to-point responses and changes made are listed below.

Comments from Reviewer #1
Overall Remarks: The paper is exploring a way to build spike-based neuromorphic perception system for tactile, optical, and temperature perception based on VO2 spiking oscillators. The concept is not new but some of the reported data and potential applications are new and innovative. The paper needs an indepth revision and many improvements before being considered for publication.
Our response: We would like to sincerely thank the reviewer for the very detailed and constructive suggestions. In this revised manuscript, we have carefully considered all 2 the points and performed new experiments, models, and simulations (Supplementary Fig. 14,15,(22)(23)(24). Our detailed responses to the comments and corresponding changes are shown as follows. Our response: We would like to thank the reviewer for the constructive comments.

1) The authors claim that this technology can be used for
The first part of the question is regarding the power consumption. We agree that power consumption is very important for neuromorphic systems. In light of this advice, 3 we have calculated the energy consumption for each spike signal in Supplementary Fig. 14, and the energy consumption for each spike is ~2.9 nJ, where transient power is calculated by multiplication of input voltage with output current and the energy consumption is calculated by dividing the total energy consumption by the spike number. The relatively high energy consumption originates from two main factors: the relatively low resistance and the relatively high Vth.
On the one hand, the resistance of the device can be improved by optimizing the growth conditions of the VO2 film. To demonstrate this, we have optimized the thin film growth conditions of VO2 and fabricated new devices. The I-V characteristic curves of the devices with different channel lengths are shown in Supplementary Fig.   15a-c. One can see that the current is reduced from mA level to 50-80 μA by optimizing film growth conditions. The resistance of the device has increased by nearly two orders of magnitude as shown in Supplementary Fig. 15d, which means that the power consumption of the device will also drop by nearly two orders of magnitude.
On the other hand, Vth and Vhold could be reduced by decreasing the channel length of the VO2 memristor as shown in Supplementary Fig. 15e. It can be seen that when the electrode width of the device is constant (1 μm), the threshold voltage indeed decreases when the channel length is decreased. We believe the power consumption could be further reduced by using a VO2 device with a lower threshold voltage. The second part of the question is how the distributions of the low and high resistance states are taken into account and modeled in the design of the sensory systems. To address this question, we now build a model of the spiking sensory neuron (Equation 2-8). Besides, we also defined the SNR (Equation 1) of spiking sensory neuron and discussed the limits in precise sensing, by calculating the SNR considering the stochastic distributions of threshold/holding voltages, and of the device using our model.
The artificial spiking neuron circuit is essentially an RC circuit. Using Kirchhoff's Current Law, we have the following differential equation: where is the capacitance in parallel to the VO2 device or can be a parasitic capacitance. is the output voltage across the VO2 device. The VO2 resistance is 2 = in HRS and 2 = in LRS. For simplicity, we assume that and are constant in our analyses.
To obtain the rising time, , from ℎ to ℎ during oscillation, we analyze the circuit when 2 = . yy integrating the equation and applying the initial condition (0) = ℎ , we obtain: Inserting these values into the equation and further rearranging, we arrive at the expression: For the falling time, , from ℎ to ℎ during oscillation, we let 2 = . yy integrating the differential equation at initial condition (0) = ℎ , we arrive at the following equation: Hence, we have: Thus, we can calculate the oscillating frequency: This model is similar to the one given in Ref. R1, R2. Extending the model to calibratable spiking sensory neuron, we have: where Rsensor, Rscaling, and Rc are resistance of sensor, scaling resistance and calibration resistance.
As can be seen from the formula, the frequency of neuron firing is affected by Vth, Vhold, and of the device. Potential fluctuations in Vth, Vhold, and will cause the fluctuation of frequency which will limits the sensing performance. To 8 better analyze the limits, we defined the SNR R3 of the spiking sensory neuron, which can be described as: To obtain the rising time, , from ℎ to ℎ during oscillation, we analyze the circuit when 2 = . By integrating the equation and applying the initial condition (0) = ℎ , we obtain: Inserting these values into the equation and further rearranging, we arrive at the expression: For the falling time, , from ℎ to ℎ during oscillation, we let 2 = . By integrating the differential equation at initial condition (0) = ℎ , we arrive at the following equation: Hence, we have: Thus, the oscillating frequency is: This model is similar to the one given in Ref.54,55 2) All the VO2 proposed spiking structures (including the ones for light and and pressure) will have a high cross-sensitivity to temperature. How the authors propose to address in a neuromorphic sensory application the intrinsic sensitivity of IMT and MIT transitions of VO2 to temperature? Such calibration and or modeling is not well explained in the paper. Moreover, the operating temperature of the proposed sensors can be highly limited by the transition temperature of the 11 VO2 (close to 68°C), which is a big limitation and concern for many electronic applications.
Our response: We would like to greatly thank the reviewer for the constructive comment. Indeed, the VO2 based spiking system is intrinsically sensitive to temperature.  Fig. 22i), the threshold/holding voltages are corrected and can be described as R4 : These relations were derived from the heat equation using lumped analysis where Rth, Tt, and T are the effective thermal resistance, the transition temperature of VO2 and the operating temperature, respectively. To obtain the rising time, , from ℎ to ℎ during oscillation, we analyze the circuit when 2 = . By integrating the equation and applying the initial condition (0) = ℎ , we obtain:

llugging these values into the equation and further
rearranging, we arrive at the expression: For the falling time, , from ℎ to ℎ during oscillation, we let 2 = . By integrating the differential equation at initial condition (0) = ℎ , we arrive at the 14 following equation: Hence, we have: Thus, oscillating frequency could be: where Rth, Tt, and T are the effective thermal resistance, the transition temperature of VO2 and the operating temperature respectively. Therefore, the impact of temperature on VO2 neuron spiking can be obtained by inserting Equation 9-10 into Equation 2-8.
The validity of this model has been verified in Supplementary Fig. 23    Our response: We would like to greatly thank the reviewer for the constructive comment.

3) The paper reports a series of experimental results without in-depth
We are sorry for the lack of models in our previous version. To address this 20 question, we have performed further modeling studies on the spiking neuron. We have added the following equations and discussions in the revised manuscript: -Page 19-20: "Moreover, we established a model of the spiking sensory neuron. The artificial spiking neuron circuit is essentially an RC circuit. Using Kirchhoff's Current Law, we have the following differential equation: where is the capacitance in parallel to the VO2 device or can be parasitic capacitance. is the output voltage across the VO2 device. The VO2 resistance is 2 = in HRS and 2 = in LRS. For simplicity, we assume that and are constant in our analyses.
To obtain the rising time, , from ℎ to ℎ during oscillation, we analyze the circuit when 2 = . By integrating the equation and applying the initial condition (0) = ℎ , we obtain: Inserting these values into the equation and further rearranging, we arrive at the expression: For the falling time, , from ℎ to ℎ during oscillation, we let 2 = . By integrating the differential equation at initial condition (0) = ℎ , we arrive at the 21 following equation: Thus, the oscillating frequency is: where Rth, Tt, and T are the effective thermal resistance, the transition temperature of VO2 and the operating temperature respectively. Therefore, the impact of temperature on VO2 neuron spiking can be obtained by inserting Equation 9-10 into Equation 2-8. 22 The validity of this model has been verified in Supplementary Fig. 23 Fig. 22l) The spiking sensory neuron is able to achieve high sensitivity of 151.74 kHz/N, 0.13 kHz/Lux and 2.8 kHz/℃ in tactile, optical, and temperature perception, respectively.
Furthermore, the signal-to-noise ratio (SNR) of the spiking sensory neuron is defined for the first time, which can be described as: where In order to benchmark with existing sensors and justify the advantages of the approach proposed here, we compared our approach with traditional silicon circuits and other spiking sensory neurons. Supplementary Fig. 24 schematically depicts the comparison between neuromorphic perception system based on silicon circuits and our 24 approach. In traditional silicon-based circuits, in order to sense physical signals a large number of ADCs (analog-to-digital converters) are necessary besides the sensors, which are very costly in area and energy consumption, and when the subsequent information processing is in spike-based neuromorphic computing systems, a large number of additional VSCs (voltage-to-spike converters) will be required R12 to realize spike conversion, which also consume a large amount of area and energy, as shown in Supplementary Fig. 24a. In stark contrast, our calibratable sensory neuron (Supplementary Fig. 24b) can directly achieve both sensing and spike conversion with the simple circuit consisting of the sensor, the VO2 memristor and a few resistors and capacitors, which is much more efficient in area and energy consumption compared to silicon circuits.
In addition, we have also compared our approach with other state-of-the-art spikebased sensory neurons, as detailed in Supplementary Table 3. Our approach has advantages in the following aspects: 1) Compared with existing works in the literature, our work achieves multiple perception modalities including pressure, light, temperature and curvature for the first time, which is due to the successful solution of the impedance matching problem between sensors and neurons using our neuron circuit. Otherwise, the neuron can fire only when the resistance range of the sensor is consistent with the resistance range of the neuron. Our neuron circuit has effectively addressed this issue by utilizing the scaling resistance and calibration resistance to adapt the working resistance ranges of different sensors, so that a variety of different modalities of perception are successfully 25 realized, which is a significant advantage of our approach and not seen in existing studies.
2) We have defined and analyzed the signal-to-noise ratio (SNR) of the spiking sensory neuron, which has not been reported in other state-of-the-art spike-based sensory neurons. The high crystalline quality of epitaxial VO2 has addressed the fundamental cycle-to-cycle and device-to-device variation issues in sensory neurons,  Fig. 15a-e), and circuit parameters such as parallel capacitance (Fig. 3, Supplementary Fig. 7-11)."

Deviations in these parameters may lead to a change in the crystal structure and/or change in the stoichiometry. Both factors lead to a change in the electronic and defect-chemical structure. Did the authors considered this specifics of VO2?
Our response: We would like to greatly thank the reviewer for the constructive comment. Indeed, VO2 is very sensitive to both temperature and oxygen content. In  Fig. 22j). As the temperature increases, one can find that the firing frequency of VO2 neurons gradually increases (576.13-656.02 kHz). We have systematically tested the dependence of the spiking frequency as a function of load resistance (RL) and temperature, and the results are displayed in Supplementary Fig.   22l, showing similar RL and T dependence in all cases. This might be ascribed to the gradual decrease in threshold voltages of VO2 memristors with increased temperature, so that the neuronal circuit requires lower voltage to fire.
In order to understand such temperature dependence, we have further constructed a model on the VO2 spiking neuron. The artificial spiking neuron circuit is essentially an RC circuit. Using Kirchhoff's Current Law, we have the following differential equation: where is the capacitance in parallel to the VO2 device or can be a parasitic capacitance. is the output voltage across the VO2 device. The VO2 resistance is 2 = in HRS and 2 = in LRS. For simplicity, we assume that and are constant in our analyses.
To obtain the rising time, , from ℎ to ℎ during oscillation, we analyze the circuit when 2 = . yy integrating the equation and applying the initial condition (0) = ℎ , we obtain: Inserting these values into the equation and further rearranging, we arrive at the expression: For the falling time, , from ℎ to ℎ during oscillation, we let 2 = . yy integrating the differential equation at initial condition (0) = ℎ , we arrive at the following equation: At = , ( ) = ℎ . Hence, we have: Thus, we can calculate the oscillating frequency: This model is similar to the one given in Ref. R1, R2. Extending the model to calibratable spiking sensory neuron, we have: where Rsensor, Rscaling, and Rc are resistance of sensor, scaling resistance and calibration resistance.
Since Vth and Vhold are temperature-dependent parameters ( Supplementary Fig. 22i), the threshold/holding voltages are corrected and can be described as R4 : These relations were derived from the heat equation using lumped analysis where Rth, Tt, and T are the effective thermal resistance, the transition temperature of VO2 and the operating temperature, respectively. Therefore, the complete spiking sensory neuron model after considering the effect of temperature can be obtained by inserting Equation The validity of this model has been verified in Supplementary Fig. 23 To obtain the rising time, , from ℎ to ℎ during oscillation, we analyze the circuit when 2 = . By integrating the equation and applying the initial condition (0) = ℎ , we obtain:

llugging these values into the equation and further
rearranging, we arrive at the expression: For the falling time, , from ℎ to ℎ during oscillation, we let 2 = . By integrating the differential equation at initial condition (0) = ℎ , we arrive at the following equation: Hence, we have: Thus, oscillating frequency could be:  Fig. 21j-k) Supplementary Fig. 22a-h. Supplementary Fig. 22i  The validity of this model has been verified in Supplementary Fig. 23 Supplementary Fig. 22l)

In same contextproperties of VO2 can also be changed by local environment and/or protons. Was this factor considered by the experiments?
Our response: We would like to thank the reviewer for the very constructive suggestion. Indeed, the local environment and/or protons is another factor(s) that can affect VO2 properties. In light of this, we have performed new studies to measure the characteristics of VO2 devices under different atmospheric pressure, including air ( Supplementary Fig. 21a), varied atmospheric pressure from 1.5×10 -3 mbar to 2×10 -4 mbar ( Supplementary Fig. 21b-h) and N2 environment ( Supplementary Fig. 21i), and hence the local environment and/or concentration of moisture/proton is systematically modulated. The experimental results showed that the VO2 device did not exhibit significant change in electrical characteristics. To quantify the impact, the threshold and holding voltages (Vth_pos, Vth_neg, Vhold_pos and Vhold_neg) as well as and of the devices at different atmospheric pressures are extracted ( Supplementary Fig. 21j-k), once again demonstrating that the VO2 memristor can operate stably under varied moisture/proton concentrations.
To address this question, the new results are now included as Supplementary Fig.   21 in the revised manuscript, along with the following discussion in Page 20 of the main text: "It is worth noting that VO2 is a system that is very sensitive to oxygen content, protons and temperature in ambient environment. In order to examine these factors, we have firstly performed control experiments to measure the characteristics of VO2 devices under different atmospheric pressure, including air ( Supplementary Fig. 21a), varied atmospheric pressure from 1.5×10 -3 mbar to 2×10 -4 mbar ( Supplementary Fig.   43 21b-h) and N2 environment (Supplementary Fig. 21i). Therefore, the concentration of oxygen and moisture/proton is gradually reduced in this process, where the VO2 device showed no significant change in its I-V characteristics. To quantify the impact, the threshold and holding voltages (Vth_pos, Vth_neg, Vhold_pos and Vhold_neg) as well as and of the devices at different atmospheric pressures are extracted ( Supplementary Fig. 21j-k). The highly stable threshold and holding voltages as well as resistance states demonstrate that the VO2

The authors are pointing out that VO2 is showing metal-insulator transition.
Within the field of RRAMS it is known that Mott-transition is meant but the more general audience of Nature Communications would be probably confused. I would recommend adding a short paragraph explaining the physics of the resistance change in VO2.
Our response: We would like to greatly thank the reviewer for the valuable suggestion.
To strengthen the discussion on the physics of resistance change in VO2 in our manuscript, we have added thermodynamic simulation by COMSOL (Supplementary  (1) to (2)), heat is generated in the VO2 memristor. Once the phase transition is triggered, a filament is formed through the VO2 gap, which switches the device from HRS to LRS.
The filament is expanded as the voltage increases (state (2) to state (3)). When the applied voltage is reduced, the heat dissipates and the filament size decreases (state (3) to state (4)). When the applied voltage is below Vhold, the filament breaks down and the device eventually returns to HRS (state (4) to state (1)

PLD is a very suitable method for preparing high quality films in lab conditions.
However, it is limited for use for applications as it cannot cover homogeneously larger surfaces (in the general case it can reliably cover about 2x2 cm^2 substrate).

It would be good to discuss what other methods can be used for larger samples.
Our response: We would like to greatly thank the reviewer for the valuable suggestion.
Although many methods have been adopted to synthesize high-quality VO2 films, the growth of wafer-scale, high-quality VO2 films with excellent phase transition property is still a challenge. The molecular beam epitaxy (MyE) method is a widely used technique to produce high-quality and homogeneous epitaxial films.  R16 . In addition, sputtering has been used to grow VO2 thin films since 1967 R17 and has shown capability in preparing VO2 films on 4-inch wafers R18 . Nevertheless, the crystalline quality of the VO2 film might be compromised in some of the above preparation processes, and the growth method should be selected based on the detailed requirements on sample scale and crystalline quality in the applications.
To address this clearly, we have added the following sentences into Page 9 of the Minor issue -On page 5 is written: "…physical signals into electrical signals…" The expression is not precise. Electricity is also a physical phenomenon. Therefore an electrical signals is a kind of physical signal.
Our response: We would like to sincerely thank the reviewer for the valuable suggestion. We have revised the expression of the article in page 5 as "In the biological perception system, certain types of receptors (photoreceptors, thermal receptors, mechanoreceptors, etc.) and neurons convert external environmental signals into electrical spikes (Fig. 1a)."   Fig. 4). Supplementary Fig. 5 Fig. 4). The device-to-device variability in Vth_pos, Vth_neg, Vhold_pos and Vhold_neg was 5.32%, 5.12%, 6.96% and 7.16%, respectively ( Supplementary Fig. 5).
Notably, Chen et al. has    3) It is good that an SNR is now defined and some benchmarks included (Table). Our response: We would like to thank the reviewer for the detailed comment. While achieving impedance matching with different sensors, the epitaxial VO2 based sensory neurons in this work still achieve high sensitivity in different sensing modes, namely, 151.74 kHz/N, 0.13 kHz/Lux, and 2.8 kHz/℃ for tactile, optical, and temperature perception, respectively. Given that our pressure sensor is 2 cm in diameter, the calculated sensitivity to pressure is 47.67 kHz/kPa, which is slightly lower than the 60.8 kHz/kPa reported in Ref. 30. This can be improved by increasing the sensitivity of the pressure sensor itself. The important point is that our spiking sensory neuron can be matched with different kinds of sensors, which is a significant advantage over existing studies. As for the sensitivity to temperature and light intensity, there seem to be no prior works reporting such metrics that can serve as the background for direct comparison, to the best of our knowledge.

However
We want to further note that our approach has advantages in the following aspects: 1) Compared with existing works in the literature, our study achieves multiple perception modalities including pressure, light, temperature and curvature for the first time, which is due to the successful solution of the impedance matching problem between sensors and neurons using our neuron circuit. Neurons can fire only when the resistance of the sensor is within a specific range. However, the resistance ranges between different sensors are very different, which is the reason why it is difficult for previous works to achieve multiple sensing modes. To match impedance with various sensors, we added a scaling resistor and calibration resistor in our neuron circuit to allow the neuron to adapt to different sensors, which is a significant advantage of our approach and is not seen in other studies.
2) We have defined and analyzed the signal-to-noise ratio (SNR) of the spiking sensory neuron, which has not been reported in other state-of-the-art spike-based sensory neurons. The high crystalline quality of epitaxial VO2 has addressed the fundamental cycle-to-cycle and device-to-device variation issues in sensory neurons, and the resultant excellent uniformity of our devices gives rise to excellent SNRs of 33.66 dB, 31.90 dB and 29.92 dB in tactile, optical and temperature sensing, respectively.
3) While achieving impedance matching with various sensors, our spiking sensory neuron can still achieve high sensitivity in tactile, optical, and temperature perception.
To the best of our knowledge, our study is the first to report the sensitivity of spiking neurons to illuminance and temperature.
To address this question, we have added the following discussion in page 15: "Given that our pressure sensor is 2 cm in diameter, the calculated sensitivity to pressure is 47.67 kHz/kPa, which is slightly lower than the 60.8 kHz/kPa reported in Ref. 30. This can be improved by increasing the sensitivity of the pressure sensor itself.
As for the sensitivity to temperature and light intensity, there seem to be no prior works reporting such metrics that can serve as the background for direct comparison, to the best of our knowledge. The important point is that our spiking sensory neuron can be matched with different kinds of sensors, which is a significant advantage over existing studies."